Developing Cloud Paramet erisat ions - t he Role of Observat ions - - - PowerPoint PPT Presentation

Developing Cloud Paramet erisat ions

• t he Role of Observat ions -

Clemens Simmer Met eorological I nst it ut e Universit y Bonn

I nit ial t hought s...

Cloud paramet ersat ions ...

... simulat e sub-scale cloud ef f ect s (geomet rical ext ensions+microphysics f or radiat ion and precipit at ion. ... were never developed direct ly f rom observat ions, ... are der ived f rom concept ual ideas about clouds (e.g. non-precipit at ing clouds exist ; t here are t rigger mechanisms f or convect ion, ...) ... are at best calibrat ed t o very limit ed observat ions

Clouds are dif f erent (see classical cloud t ypes)

... some simple clouds led t o cloud par amet erisat ion concept s ... ... cloud par amet erisat ion relat e t o special cloud t ypes ... and must be biased when used in a generalized manner, as t hey are.

Clouds are an int egral part of t he st at e of t he at mosphere...

... but are t reat ed as an added-on, re-act ing phenomenon. ... inst ead cloud par amet erisat ions should be t wo-way-coupled wit h large- scale st at e, t urbulence, convect ion and radiat ion processes.

• >

isolat ed cloud paramet erisat ions are always incomplet e (meaning t hey hard t o validat e).

Cont ent s

• What are cloud paramet erisat ions?
• How can observat ions be used t o

calibrat e cloud paramet erisat ions?

• What kind of observat ions do we

really need, t o subst ant iat e or calibrat e cloud paramet erisat ions?

• Are t here ot her ways t o use
• bservat ions?

What are cloud paramet erisat ions?

... calibrat ed f ormalized concept ual models about cloud processes and st ruct ur es at scales below t he models grid and t emporal resolut ions ... ... in order t o diagnose f ract ional cloud cover, cloud microphysical paramet ers (part icle number concent rat ions, cloud bulk densit ies, part icle size dist ribut ions,...) ... t o allow calculat ion of radiat ive ef f ect s, ... t o allow f or microphysical processes, i.e. precipit at ion simulat ions.

Connect ions bet ween cloud and ot her paramet ersat ions (complet e physics package)

Convect ion paramet ersat ion diagnose at mospheric mot ion ef f ect s, like energy, moment um, and mass f luxes on sub-grid scales cloud and convect ion paramet erisat ions must be physically very st rongly relat ed, but t hey are t radit ionally t reat ed independent ly. The same holds f or t urbulence and radiat ion modules, and also includes t he core model.

Types of cloud paramet erisat ions

• Det erminist ic schemes

gridscale values lead t o unique set s cloud paramet ers

• probabilist ic schemes

gridscale values lead t o dist ribut ions of cloud paramet ers

Cont ent s

• What are cloud paramet erisat ions?
• How are observat ions used t o aid

cloud paramet erisat ions?

• What kind of observat ions do we

really need, t o subst ant iat e or calibrat e cloud paramet erisat ions?

• Are t here ot her ways t o use
• bservat ions?

Ways t o aid t he development of cloud paramet erisat ions

• Proof of concept

...needs dedicat ed experiment set ups

• Calibrat ion of f ormalized concept s

... can of t en be achieved wit h t radit ional experiment set ups and long-t erm measurement s (but you need t o very caref ul)

Cont ent s

• What are cloud paramet erisat ions?
• How are observat ions used t o

calibrat e cloud paramet erisat ions?

• What kind of observat ions do we

need, t o subst ant iat e or calibrat e cloud paramet erisat ions?

• Are t here ot her ways t o use
• bservat ions?

Opt imal measurement s

• Cont inuous long t erm high t emporal resolut ion measurement s are

indispensible f or st at ist ical reasons

• Surf ace radiat ion measurement s (F)
• Temperat ure (T(z) ) and humidit y prof ile (RH(z) ) f rom radiosondes

(RS), radioacoust ic sounding syst ems ( RASS), or microwave prof ilers (MWP)

• Passive microwaves (MWR) f or t ot al wat er vapour (W) and cloud

liquid wat er pat h (LWP)

• Precipit at ion radar (PR) f or in-cloud precipit at ion-size part icles

(RRt y(z))

• FMCW-radar (MRR) f or precipit at ion part icle size dist ribut ions

(N(DRRz))

• Cloud radar (CR) and laser-ceilomet er (LC) f or cloud cover (N(z))
• MWR+CR+LC f or LWC(z), I WC(z)
• Aircraf t measurement s f or cloud microphysiscs, wat er vapor

variat ions, t urbulence...

• Scanning wat er vapor lidar t o det et ct cont inuously spat ial and

t emporal wat er vapor variat ions

• High t emporal f ields of cloud paramet ers f rom sat ellit es
• High qualit y f orcing f ields (analysis)

Similar t o BBC(1)

+ anot her aircraf t + Raman lidar + micro rain radar(s) + (growing like ...)

What did we learn f rom CLI WA-Net

• Percept ion/ assumpt ions of clouds f rom modellers and
• bservers can be very dif f erent (e.g. LWP wit h/ wit hout

drizzle or rain, what is a cloud, what is cloud cover).

• Modellers always t hink, t hat measurement s have no errors,

at least t hey assume, t he are gaussian). When t hey lear n about errors t hey t end t o discard any measurement s.

• Bot h models and obser vat ions are biased in very dif f erent

ways (daily variat ions, pr ecipit at ion) leading t o dif f erent ly biased st at ist ics.

• The impact of measurement s on paramet erisat ions was nil,

unt il conf idence was est ablished bet ween modeller s and

• bservers (modellers need t o underst and measurement s and

vice ver sa).

Specif ic result s of CLI WA-Net

• LWP-f ields wit h reasonable error (30%) f rom

sat ellit es available f or model comparisons

• High-qualit y (Low-cost ) prof iler (radiomet er)

available f or ground-based LWP net work

• Algorit hms f or condensed wat er prof iles f rom

ground-based synerget ic measurement s (cloud radar + microwave prof ile + laser ceilomet er)

• Assessment of cloud paramet ers f rom st at e-of -

t he–art at mospheric models

• Preliminary quant if icat ions of model short comings

and errors in assumpt ions in cloud paramet erisat ions (e.g. cloud overlap assumpt ions)

BBC-Cabauw: Measured and model predict ed ver t ical dist ribut ion of liquid wat er cont ent , LWC(z)

1 August 2001

13 August 2001

14 September 2001

RCA model: Impact of vertical model resolution: 24Levels 40Levels 60Levels

Budgets and fluxes I

Reference values:

7km run without convection scheme

relative deviations

runs with 7, 2.8 and 1.1 km grid spacings

Example: 13. April 2001

average over model domain and 24h

50 100 150 200

Water vapour 6.5kg/m2 Ice 3.4g/m2 Liquid water 7.6g/m2 Cloud cover 59% Radiation 80W/m2 Evapo. 99W/m2 Rain 0.9mm/d

50 100 150 200 250 300

50 100 150 200

50 100 150 200 50 100 150 200 50 100 150 200 50 100 150 200

50 100 150 200

Heatflux 36W/m2

Results:

• water vapour,

cloud cover and surface fluxes remain unchanged

• LWP and rain rate

increase due to refinement

Results:

• water vapour,

cloud cover and surface fluxes remain unchanged

• LWP and rain rate

increase due to refinement

Nonlinear LWP-rain relation

grid refinement

Result of LM cloud scheme using idealized cloud profiles

more LWP variations nonlinear LWP-RR relation more RAIN !

25 50 75 100 125 150 175 200

averaged rainfall

LWP histograms

Example 13 April 01

Domain average Cabauw

Probably poor statistic!

Cont ent s

• What are cloud paramet erisat ions?
• How are observat ions used t o

calibrat e cloud paramet erisat ions?

• What kind of observat ions do we

really need, t o subst ant iat e or calibrat e cloud paramet erisat ions?

• Are t here ot her ways t o use
• bservat ions?

Are t here ot her ways...?

• Proof of exist ing concept s
• >dedicat ed experiment s (or dedicat ed

analysis of exist ing experiment s) f or clearly def ined cloud t ype concept s in order t o prove

• r even bet t er t o f alsif y t he concept
• St at ist ical- probabilist ic approach

(e.g. neural net works) wit hout init ial concept s

• >very many dat a, very long t ime series,

analysis might , or might not lead t o new (or old) concept s